Abstract
In this paper, simulations of the performance of a building that has different energy flexibility sources is conducted. The building is a simulated single-family house located in Helsinki-Finland. The building’s energy system components include on-site energy generation from renewable energy (PV panels, solar thermal collector and a small wind turbine), energy storage (electric battery and a hot-water storage tank HWST) and heating devices (ground-source heat pump and electric heater), interacting with a bidirectional electric grid.
An energy management system (EMS) is developed for optimizing the system’s energy flexibility performance at each time step considering the current states and the future forecast of the system’s energy generation, demand, storage and the electrical grid. The objective function in the studied case is to minimize the operational energy cost. The EMS is a model predictive controller (MPC) based on Successive Linear Programming (SLP), which plans the energy flow for the next 24-h sliding window with 0.1 h time step. The SLP method approximates the scheduling as a linear optimization problem with continuous non-linear constraints.
In the current study, different HWST volumes and battery capacities are investigated in order to find the effect of the storage capacity on the system’s economic performance. The developed EMS is found to be very fast and efficient for simulations of the whole-year performance of the energy system. It is concluded that increasing the size of the battery is more effective than increasing the size of the HWST. In addition, the larger size of the tank showed an adverse effect on the total yearly income as smaller tanks are found to be more viable. This is mainly due to the used configuration of the HWST that combines both the space heating and domestic hot-water use, in addition to the limitation in the heat pump supply temperature to lower than 60 °C
An energy management system (EMS) is developed for optimizing the system’s energy flexibility performance at each time step considering the current states and the future forecast of the system’s energy generation, demand, storage and the electrical grid. The objective function in the studied case is to minimize the operational energy cost. The EMS is a model predictive controller (MPC) based on Successive Linear Programming (SLP), which plans the energy flow for the next 24-h sliding window with 0.1 h time step. The SLP method approximates the scheduling as a linear optimization problem with continuous non-linear constraints.
In the current study, different HWST volumes and battery capacities are investigated in order to find the effect of the storage capacity on the system’s economic performance. The developed EMS is found to be very fast and efficient for simulations of the whole-year performance of the energy system. It is concluded that increasing the size of the battery is more effective than increasing the size of the HWST. In addition, the larger size of the tank showed an adverse effect on the total yearly income as smaller tanks are found to be more viable. This is mainly due to the used configuration of the HWST that combines both the space heating and domestic hot-water use, in addition to the limitation in the heat pump supply temperature to lower than 60 °C
Original language | English |
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Title of host publication | Renewable Energy and Sustainable Buildings |
Subtitle of host publication | Selected Papers from the World Renewable Energy Congress WREC 2018 |
Editors | Ali Sayigh |
Publisher | Springer |
Pages | 507-516 |
ISBN (Electronic) | 978-3-030-18488-9 |
ISBN (Print) | 978-3-030-18487-2 |
DOIs | |
Publication status | Published - 23 Sept 2019 |
MoE publication type | A3 Part of a book or another research book |
Event | World Renewable Energy Congress, WREC 2018 - Kingston upon Thames, United Kingdom Duration: 29 Jul 2018 → 3 Aug 2018 |
Publication series
Series | Innovative Renewable Energy (INREE) |
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ISSN | 2522-8927 |
Conference
Conference | World Renewable Energy Congress, WREC 2018 |
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Country/Territory | United Kingdom |
City | Kingston upon Thames |
Period | 29/07/18 → 3/08/18 |